The invention relates generally to the field of intrusion detection and burglar alarm systems. Specifically, the methodology employed also is related to pattern recognition, fuzzy logic, and neural nets.
A personal identification system, such as that which might be employed as part of an intrusion detection mechanism, often entails pattern recognition to analyze a set of input variables with respect to a set of known values from which can be derived a match conclusion. Such pattern recognition is not as discrete as arithmetic equality, but rather analyzes the input variables and known values against an accepted tolerance of variation to determine a pattern match. A greater number of input variables and a lower accepted tolerance results improves accurate recognition, reducing false positive matches, but also increasing false negatives, which could lead to, for example, denial of an authorized party.
One prior art method for pattern recognition employs neural nets. Neural nets are specialized hardware which is connection-based, rather than the traditional rule-based platform. Such platforms utilize multiple connections between processors, rather than multiple rules applied sequentially to a single processor, to produce a result. Harrison (U.S. Pat. No. 5,576,972) utilizes multiple sensors surveying an area combined with a neural net approach to use sensor output to define a model of an area, which, if it is disturbed, signifies intrusion. Adams (U.S. Pat. No. 5,313,558) uses neural nets to apply a mathematical approach which introduces sequential events arrayed in time as a dimension of the pattern.
Other prior art pattern recognition systems utilize fuzzy logic. Sultan et al. (U.S. Pat. No. 5,499,319) describes a best-fit fuzzy logic metric oriented toward industrial control. Tanaka (U.S. Pat. No. 5,479,533) describes written or printed character recognition using fuzzy logic oriented around complex, hierarchically organized patterns. Verly et al. (U.S. Pat. No. 5,123,057) matches a list-structured hierarchy of events, and develops a metric for the degree of match.
Pattern matching systems such as those described above have been used to assess behavioral patterns. Such patterns, based on a profile which has been judged typical or symptomatic of a class of people likely to have criminal intent, are described in McNair (U.S. Pat. No. 5,375,244) which teaches a system to control access to a computer by observing various attributes of the log-on sequence, and computing the "distance" in multidimensional space from a cluster of attributes comprising the profile of legitimate users versus hackers. This approach differs from the present invention because the purpose of the measurements in the invention is to compare the resulting profile against the specific attributes of known subjects rather than merely comparing variables to a statistically determined unacceptable profile.
Also, Prezioso (U.S. Pat. No. 5,577,169) shows a system for searching a database containing information about medical insurance claims, and comparing the attributes with a pattern deemed to represent fraudulent health care providers using fuzzy logic algorithms.
Traditional intrusion detection systems such as burglar alarm systems typically sense presence, motion, and action, but do not build up a composite image to identify individuals. Authorized individuals are identified by possession of a single discrete element such as a mechanical, electronic, or magnetic card or key, or by relatively expensive techniques to measure a single characteristic such as voice signature analysis, iris shape, or fingerprint. Examples of the latter are shown in Daugman (U.S. Pat. No. 5,291,560), describing pattern recognition based on iris analysis, and Sidlauskas (U.S. Pat. No. 4,736,203), teaching three dimensional hand profile analysis. These types of systems involve relatively high costs due to the expense required to achieve precision and accuracy concerning measurement of essentially a single attribute of an individual.